Asymptotics of hierarchical clustering for growing dimension

JOURNAL OF MULTIVARIATE ANALYSIS

卷:124

Article

Borysov, Petro^1,2  Hannig, Jan²  Marron, J. S.² 

[1] SAS Inst, Cary, NC 27513 USA

[2] Univ N Carolina, Dept Stat & Operat Res, Chapel Hill, NC 27599 USA

关键词: Hierarchical clustering;    Linkage function;    Clustering behavior;   

DOI  :  10.1016/j.jmva.2013.11.010

来源: Elsevier

【 摘 要 】

Modern day science presents many challenges to data analysts. Advances in data collection provide very large (number of observations and number of dimensions) data sets. In many areas of data analysis an informative task is to find natural separations of data into homogeneous groups, i.e. clusters. In this paper we study the asymptotic behavior of hierarchical clustering in situations where both sample size and dimension grow to infinity. We derive explicit signal vs noise boundaries between different types of clustering behaviors. We also show that the clustering behavior within the boundaries is the same across a wide spectrum of asymptotic settings. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free